Effectiveness of Social Enterprise in Managing Intellectual Capital
Bibliographic record
Abstract
In order to successfully accomplish the social and business mission, social enterprises need to identify the appropriate elements of resources that affect their performance since the management of resources is important to ensure the effectiveness of social enterprise. Thus, this study aims to examine the role of intellectual capital, in terms of human capital, structural capital and relational capital on the effectiveness of social enterprise which is represented by the financial viability. Information on the financial viability and intellectual capital were obtained from the content analysis of the annual reports of 210 social enterprises registered under the Registry of Societies (ROS) in Malaysia for the financial period 2010. The results from the statistical analysis revealed that on average, most of the social enterprises in Malaysia would be able to financially sustain in the future. Based on the multivariate analysis, the results highlighted that human capital has a significant positive influence on the financial viability of social enterprise while structural capital and relational capital do not have significant positive relationship with the financial viability of social enterprise. Overall, the findings concluded that human capital was the most influential factor in enabling the effectiveness of social enterprise.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".